In this issue of ckj, Sever et al. (A roadmap for optimizing chronic kidney disease patient care and patient-oriented research in the Eastern European nephrology community. Clin Kidney J, this issue) present a roadmap for optimizing chronic kidney disease (CKD) patient care and patient-oriented research in Eastern Europe. The document clearly identifies current unmet needs and proposes corrective actions. Focusing on CKD epidemiology and outcomes, it collects evidence pointing to an East-West gradient for some key risk factors for CKD development. Thus, the prevalence of diabetes, raised blood pressure, obesity and tobacco use is higher in Eastern than in Western Europe. These risk factors may contribute to the higher CKD prevalence in Eastern Europe, which for the Eastern-most countries may be more than 2-fold higher than in Western Europe. The problem is compounded by the lower prevalence of dialysis and transplantation in Eastern Europe, especially in lower income countries. The combination of higher prevalence of CKD with lower prevalence of renal replacement therapy would be expected to result in higher CKD-associated mortality, but this is not the case. CKD-associated mortality may even be lower in the Eastern-most European countries than in Western Europe. The reasons for this discrepancy should be studied, since it may reveal serious additional healthcare issues, potentially related to high mortality from other non-communicable diseases (NCDs). If this is the case and the high mortality from other NCD is successfully addressed, pressure will further mount on renal replacement capacity needs in Eastern Europe.Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (mSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from ~3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN.The JRC COVID-19 In Vitro Diagnostic Devices and Test Methods Database, aimed to collect in a single place all publicly available information on performance of CE-marked in vitro diagnostic medical devices (IVDs) as well as in house laboratory-developed devices and related test methods for COVID-19, is here presented. The database, manually curated and regularly updated, has been developed as a follow-up to the Communication from the European Commission "Guidelines on in vitro diagnostic tests and their performance" of 15 April 2020 and is freely accessible at https//covid-19-diagnostics.jrc.ec.europa.eu/.Background Depression is common in multiple sclerosis (MS); however, its assessment is complicated by biological processes. In this context it is important to consider the performance of depression screening measures including that their factor structure is consistent with expectation. This study sought to identify the factor structure of the Center for Epidemiological Study - Depression Scale (CES-D) in people with MS (PwMS). Methods Participants (N = 493) were those who had consented to take part in a large three-phase longitudinal study of depression in PwMS. CES-D questionnaires completed at phase 1 of the study were utilised. An error in the questionnaire meant it was most appropriate to consider data for 19 of the 20 CES-D questionnaire items. The data was split into two samples by a random selection process to create an exploratory, model development sample and a validation sample. The first sample was subject to confirmatory factor analysis. https://www.selleckchem.com/ Following examination of model fit and specification errors, the original model was modified. The revised model was tested in the confirmation sample to assess reproducibility. Results The analysis results supported the original four factor solution for the CES-D, that is Depressed Affect, Positive Affect, Somatic Complaints/Activity Inhibition, and Interpersonal Difficulties. Conclusions The CES-D appears to have a coherent structure with which to examine depression in PwMS.Case This report describes a clinical case of unilateral condylar hyperplasia (CH) with unique, atypical morphology. An important feature of this report is the documentation of a series of clinical photographs of the patient, showing a gradual increase in facial asymmetry associated with the CH. The main symptom reported in this case was facial asymmetry. The main intraoral clinical features observed in the patient were contralateral crossbite and ipsilateral open bite associated with CH. Surgical reshaping of the condyle was the treatment plan for this case. Conclusions The main take away point from this case is the importance of obtaining previous photographs of the patient at different ages during case diagnosis, which helps the clinician to determine the approximate time of commencement of CH. This case also highlights the imaging features of rarely observed atypical shape of the hyperplastic condyle.Lipidomics increasingly describes the quantification using mass spectrometry of all lipids present in a biological sample. As the power of lipidomics protocols increase, thousands of lipid molecular species from multiple categories can now be profiled in a single experiment. Observed changes due to biological differences often encompass large numbers of structurally-related lipids, with these being regulated by enzymes from well-known metabolic pathways. As lipidomics datasets increase in complexity, the interpretation of their results becomes more challenging. BioPAN addresses this by enabling the researcher to visualise quantitative lipidomics data in the context of known biosynthetic pathways. BioPAN provides a list of genes, which could be involved in the activation or suppression of enzymes catalysing lipid metabolism in mammalian tissues.